CRISIS: Integrating AIS and Ocean Data Streams Using Semantic Web Standards for Event Detection

Abstract

Information deluge is still an issue in the maritime environment, creating situations where data are sometimes underutilized or in more extreme cases, not utilized, in the decision-making process. In part, this is due to the high volume of incoming data that are available to the operational community. However, better exploitation of these data streams can be accomplished through techniques that focus on the semantics of the incoming stream, to discover information-based alerts that generate knowledge that is only obtainable when considering the totality of the streams. In this paper, we present an agile data architecture for real-time data representation, integration, and querying situations over heterogeneous data streams using Semantic Web Technologies, with the goal of improved knowledge interoperability. We apply the framework to the maritime ship traffic domain to discover real-time traffic alerts by querying and reasoning across multiple streams.